Post on 27-Jan-2015
description
April 10, 2023 Data Mining: Concepts and Techniques
1
Data Mining: Concepts and Techniques
— Chapter 11 —
— Applications and Trends in Data Mining—
Jiawei Han and Micheline Kamber
Department of Computer Science
University of Illinois at Urbana-Champaign
www.cs.uiuc.edu/~hanj©2006 Jiawei Han and Micheline Kamber. All rights reserved.
April 10, 2023 Data Mining: Concepts and Techniques
2
April 10, 2023 Data Mining: Concepts and Techniques
3
Applications and Trends in Data Mining
Data mining applications
Data mining system products and research prototypes
Additional themes on data mining
Social impacts of data mining
Trends in data mining
Summary
April 10, 2023 Data Mining: Concepts and Techniques
4
Data Mining Applications
Data mining is an interdisciplinary field with wide and diverse applications There exist nontrivial gaps between data
mining principles and domain-specific applications
Some application domains Financial data analysis Retail industry Telecommunication industry Biological data analysis
April 10, 2023 Data Mining: Concepts and Techniques
5
Data Mining for Financial Data Analysis
Financial data collected in banks and financial institutions are often relatively complete, reliable, and of high quality
Design and construction of data warehouses for multidimensional data analysis and data mining View the debt and revenue changes by month, by
region, by sector, and by other factors Access statistical information such as max, min,
total, average, trend, etc. Loan payment prediction/consumer credit policy
analysis feature selection and attribute relevance ranking Loan payment performance Consumer credit rating
April 10, 2023 Data Mining: Concepts and Techniques
6
Financial Data Mining
Classification and clustering of customers for targeted marketing multidimensional segmentation by nearest-
neighbor, classification, decision trees, etc. to identify customer groups or associate a new customer to an appropriate customer group
Detection of money laundering and other financial crimes integration of from multiple DBs (e.g., bank
transactions, federal/state crime history DBs) Tools: data visualization, linkage analysis,
classification, clustering tools, outlier analysis, and sequential pattern analysis tools (find unusual access sequences)
April 10, 2023 Data Mining: Concepts and Techniques
7
Data Mining for Retail Industry
Retail industry: huge amounts of data on sales, customer shopping history, etc.
Applications of retail data mining Identify customer buying behaviors Discover customer shopping patterns and trends Improve the quality of customer service Achieve better customer retention and satisfaction Enhance goods consumption ratios Design more effective goods transportation and
distribution policies
April 10, 2023 Data Mining: Concepts and Techniques
8
Data Mining in Retail Industry (2)
Ex. 1. Design and construction of data warehouses based on the benefits of data mining Multidimensional analysis of sales, customers,
products, time, and region Ex. 2. Analysis of the effectiveness of sales campaigns Ex. 3. Customer retention: Analysis of customer loyalty
Use customer loyalty card information to register sequences of purchases of particular customers
Use sequential pattern mining to investigate changes in customer consumption or loyalty
Suggest adjustments on the pricing and variety of goods
Ex. 4. Purchase recommendation and cross-reference of items
April 10, 2023 Data Mining: Concepts and Techniques
9
Data Mining for Telecomm. Industry (1)
A rapidly expanding and highly competitive industry and a great demand for data mining Understand the business involved Identify telecommunication patterns Catch fraudulent activities Make better use of resources Improve the quality of service
Multidimensional analysis of telecommunication data Intrinsically multidimensional: calling-time,
duration, location of caller, location of callee, type of call, etc.
April 10, 2023 Data Mining: Concepts and Techniques
10
Data Mining for Telecomm. Industry (2)
Fraudulent pattern analysis and the identification of unusual
patterns
Identify potentially fraudulent users and their atypical usage
patterns
Detect attempts to gain fraudulent entry to customer accounts
Discover unusual patterns which may need special attention
Multidimensional association and sequential pattern analysis
Find usage patterns for a set of communication services by
customer group, by month, etc.
Promote the sales of specific services
Improve the availability of particular services in a region
Use of visualization tools in telecommunication data analysis
April 10, 2023 Data Mining: Concepts and Techniques
11
Biomedical Data Analysis
DNA sequences: 4 basic building blocks (nucleotides): adenine (A), cytosine (C), guanine (G), and thymine (T).
Gene: a sequence of hundreds of individual nucleotides arranged in a particular order
Humans have around 30,000 genes Tremendous number of ways that the nucleotides
can be ordered and sequenced to form distinct genes Semantic integration of heterogeneous, distributed
genome databases Current: highly distributed, uncontrolled
generation and use of a wide variety of DNA data Data cleaning and data integration methods
developed in data mining will help
April 10, 2023 Data Mining: Concepts and Techniques
12
DNA Analysis: Examples
Similarity search and comparison among DNA sequences Compare the frequently occurring patterns of each class (e.g.,
diseased and healthy) Identify gene sequence patterns that play roles in various
diseases Association analysis: identification of co-occurring gene sequences
Most diseases are not triggered by a single gene but by a combination of genes acting together
Association analysis may help determine the kinds of genes that are likely to co-occur together in target samples
Path analysis: linking genes to different disease development stages Different genes may become active at different stages of the
disease Develop pharmaceutical interventions that target the different
stages separately Visualization tools and genetic data analysis
April 10, 2023 Data Mining: Concepts and Techniques
13
Applications and Trends in Data Mining
Data mining applications
Data mining system products and research prototypes
Additional themes on data mining
Social impacts of data mining
Trends in data mining
Summary
April 10, 2023 Data Mining: Concepts and Techniques
14
How to Choose a Data Mining System?
Commercial data mining systems have little in common Different data mining functionality or methodology May even work with completely different kinds of
data sets Need multiple dimensional view in selection Data types: relational, transactional, text, time
sequence, spatial? System issues
running on only one or on several operating systems?
a client/server architecture? Provide Web-based interfaces and allow XML data as
input and/or output?
April 10, 2023 Data Mining: Concepts and Techniques
15
How to Choose a Data Mining System? (2)
Data sources ASCII text files, multiple relational data sources support ODBC connections (OLE DB, JDBC)?
Data mining functions and methodologies One vs. multiple data mining functions One vs. variety of methods per function
More data mining functions and methods per function provide the user with greater flexibility and analysis power
Coupling with DB and/or data warehouse systems Four forms of coupling: no coupling, loose coupling,
semitight coupling, and tight coupling Ideally, a data mining system should be tightly coupled
with a database system
April 10, 2023 Data Mining: Concepts and Techniques
16
How to Choose a Data Mining System? (3)
Scalability Row (or database size) scalability Column (or dimension) scalability Curse of dimensionality: it is much more challenging
to make a system column scalable that row scalable Visualization tools
“A picture is worth a thousand words” Visualization categories: data visualization, mining
result visualization, mining process visualization, and visual data mining
Data mining query language and graphical user interface Easy-to-use and high-quality graphical user interface Essential for user-guided, highly interactive data
mining
April 10, 2023 Data Mining: Concepts and Techniques
17
Examples of Data Mining Systems (1)
Mirosoft SQLServer 2005 Integrate DB and OLAP with mining Support OLEDB for DM standard
SAS Enterprise Miner A variety of statistical analysis tools Data warehouse tools and multiple data mining
algorithms IBM Intelligent Miner
A wide range of data mining algorithms Scalable mining algorithms Toolkits: neural network algorithms, statistical
methods, data preparation, and data visualization tools
Tight integration with IBM's DB2 relational database system
April 10, 2023 Data Mining: Concepts and Techniques
18
Examples of Data Mining Systems (2)
SGI MineSet Multiple data mining algorithms and advanced
statistics Advanced visualization tools
Clementine (SPSS) An integrated data mining development
environment for end-users and developers Multiple data mining algorithms and
visualization tools
April 10, 2023 Data Mining: Concepts and Techniques
19
Applications and Trends in Data Mining
Data mining applications
Data mining system products and research prototypes
Additional themes on data mining
Social impacts of data mining
Trends in data mining
Summary
April 10, 2023 Data Mining: Concepts and Techniques
20
Visual Data Mining
Visualization: use of computer graphics to create visual images which aid in the understanding of complex, often massive representations of data
Visual Data Mining: the process of discovering implicit but useful knowledge from large data sets using visualization techniques
Computer Graphics
High Performance Computing
Pattern Recognition
Human Computer Interfaces
Multimedia Systems
April 10, 2023 Data Mining: Concepts and Techniques
21
Visualization
Purpose of Visualization Gain insight into an information space by
mapping data onto graphical primitives Provide qualitative overview of large data sets Search for patterns, trends, structure,
irregularities, relationships among data. Help find interesting regions and suitable
parameters for further quantitative analysis. Provide a visual proof of computer
representations derived
April 10, 2023 Data Mining: Concepts and Techniques
22
Visual Data Mining & Data Visualization
Integration of visualization and data mining data visualization data mining result visualization data mining process visualization interactive visual data mining
Data visualization Data in a database or data warehouse can be
viewed at different levels of abstraction as different combinations of attributes or
dimensions Data can be presented in various visual forms
April 10, 2023 Data Mining: Concepts and Techniques
23
Data Mining Result Visualization
Presentation of the results or knowledge obtained from data mining in visual forms
Examples Scatter plots and boxplots (obtained from
descriptive data mining) Decision trees Association rules Clusters Outliers Generalized rules
April 10, 2023 Data Mining: Concepts and Techniques
24
Boxplots from Statsoft: Multiple Variable Combinations
April 10, 2023 Data Mining: Concepts and Techniques
25
Visualization of Data Mining Results in SAS Enterprise Miner: Scatter Plots
April 10, 2023 Data Mining: Concepts and Techniques
26
Visualization of Association Rules in SGI/MineSet 3.0
April 10, 2023 Data Mining: Concepts and Techniques
27
Visualization of a Decision Tree in SGI/MineSet 3.0
April 10, 2023 Data Mining: Concepts and Techniques
28
Visualization of Cluster Grouping in IBM Intelligent Miner
April 10, 2023 Data Mining: Concepts and Techniques
29
Data Mining Process Visualization
Presentation of the various processes of data mining in visual forms so that users can see
Data extraction process
Where the data is extracted
How the data is cleaned, integrated, preprocessed, and mined
Method selected for data mining
Where the results are stored
How they may be viewed
April 10, 2023 Data Mining: Concepts and Techniques
30
Visualization of Data Mining Processes by Clementine
Understand variations with visualized data
See your solution discovery process clearly
April 10, 2023 Data Mining: Concepts and Techniques
31
Interactive Visual Data Mining
Using visualization tools in the data mining process to help users make smart data mining decisions
Example Display the data distribution in a set of attributes
using colored sectors or columns (depending on whether the whole space is represented by either a circle or a set of columns)
Use the display to which sector should first be selected for classification and where a good split point for this sector may be
April 10, 2023 Data Mining: Concepts and Techniques
32
Interactive Visual Mining by Perception-Based Classification
(PBC)
April 10, 2023 Data Mining: Concepts and Techniques
33
Audio Data Mining
Uses audio signals to indicate the patterns of data or the features of data mining results
An interesting alternative to visual mining An inverse task of mining audio (such as music)
databases which is to find patterns from audio data
Visual data mining may disclose interesting patterns using graphical displays, but requires users to concentrate on watching patterns
Instead, transform patterns into sound and music and listen to pitches, rhythms, tune, and melody in order to identify anything interesting or unusual
April 10, 2023 Data Mining: Concepts and Techniques
34
Applications and Trends in Data Mining
Data mining applications
Data mining system products and research prototypes
Additional themes on data mining
Social impacts of data mining
Trends in data mining
Summary
April 10, 2023 Data Mining: Concepts and Techniques
35
Scientific and Statistical Data Mining (1)
There are many well-established statistical techniques for data analysis, particularly for numeric data
applied extensively to data from scientific experiments and data from economics and the social sciences
Regression predict the value of a response (dependent) variable from one or more predictor (independent) variables where the variables are numeric forms of regression: linear, multiple, weighted, polynomial, nonparametric, and robust
April 10, 2023 Data Mining: Concepts and Techniques
36
Scientific and Statistical Data Mining (2)
Generalized linear models allow a categorical response variable
(or some transformation of it) to be related to a set of predictor variables
similar to the modeling of a numeric response variable using linear regression
include logistic regression and Poisson regression
Mixed-effect models For analyzing grouped data, i.e. data that can be classified according to one or more grouping variables Typically describe relationships between a response variable and some covariates in data grouped according to one or more factors
April 10, 2023 Data Mining: Concepts and Techniques
37
Scientific and Statistical Data Mining (3)
Regression trees Binary trees used for classification
and prediction Similar to decision trees:Tests are
performed at the internal nodes In a regression tree the mean of
the objective attribute is computed and used as the predicted value
Analysis of variance Analyze experimental data for two
or more populations described by a numeric response variable and one or more categorical variables (factors)
April 10, 2023 Data Mining: Concepts and Techniques
38
Scientific and Statistical Data Mining (4)
Factor analysis determine which variables are
combined to generate a given factor
e.g., for many psychiatric data, one can indirectly measure other quantities (such as test scores) that reflect the factor of interest
Discriminant analysis predict a categorical response
variable, commonly used in social science
Attempts to determine several discriminant functions (linear combinations of the independent variables) that discriminate among the groups defined by the response variable
www.spss.com/datamine/factor.htm
April 10, 2023 Data Mining: Concepts and Techniques
39
Scientific and Statistical Data Mining (5)
Time series: many methods such as autoregression, ARIMA (Autoregressive integrated moving-average modeling), long memory time-series modeling
Quality control: displays group summary charts Survival analysis
predicts the probability that a patient undergoing a medical treatment would survive at least to time t (life span prediction)
April 10, 2023 Data Mining: Concepts and Techniques
40
Theoretical Foundations of Data Mining (1)
Data reduction The basis of data mining is to reduce the data
representation Trades accuracy for speed in response
Data compression The basis of data mining is to compress the
given data by encoding in terms of bits, association rules, decision trees, clusters, etc.
Pattern discovery The basis of data mining is to discover patterns
occurring in the database, such as associations, classification models, sequential patterns, etc.
April 10, 2023 Data Mining: Concepts and Techniques
41
Theoretical Foundations of Data Mining (2)
Probability theory The basis of data mining is to discover joint
probability distributions of random variables Microeconomic view
A view of utility: the task of data mining is finding patterns that are interesting only to the extent in that they can be used in the decision-making process of some enterprise
Inductive databases Data mining is the problem of performing inductive
logic on databases, The task is to query the data and the theory (i.e.,
patterns) of the database Popular among many researchers in database
systems
April 10, 2023 Data Mining: Concepts and Techniques
42
Data Mining and Intelligent Query Answering
A general framework for the integration of data mining and intelligent query answering Data query: finds concrete data stored in a
database; returns exactly what is being asked Knowledge query: finds rules, patterns, and
other kinds of knowledge in a database Intelligent (or cooperative) query answering:
analyzes the intent of the query and provides generalized, neighborhood or associated information relevant to the query
April 10, 2023 Data Mining: Concepts and Techniques
43
Applications and Trends in Data Mining
Data mining applications
Data mining system products and research prototypes
Additional themes on data mining
Social impacts of data mining
Trends in data mining
Summary
April 10, 2023 Data Mining: Concepts and Techniques
44
Is Data Mining a Hype or Will It Be Persistent?
Data mining is a technology Technological life cycle
Innovators Early adopters Chasm Early majority Late majority Laggards
April 10, 2023 Data Mining: Concepts and Techniques
45
Life Cycle of Technology Adoption
Data mining is at Chasm!? Existing data mining systems are too generic Need business-specific data mining solutions
and smooth integration of business logic with data mining functions
April 10, 2023 Data Mining: Concepts and Techniques
46
Data Mining: Managers' Business or Everyone's?
Data mining will surely be an important tool for managers’ decision making Bill Gates: “Business @ the speed of thought”
The amount of the available data is increasing, and data mining systems will be more affordable
Multiple personal uses Mine your family's medical history to identify
genetically-related medical conditions Mine the records of the companies you deal with Mine data on stocks and company performance,
etc. Invisible data mining
Build data mining functions into many intelligent tools
April 10, 2023 Data Mining: Concepts and Techniques
47
Social Impacts: Threat to Privacy and Data Security?
Is data mining a threat to privacy and data security? “Big Brother”, “Big Banker”, and “Big Business” are
carefully watching you Profiling information is collected every time
credit card, debit card, supermarket loyalty card, or frequent flyer card, or apply for any of the above
You surf the Web, rent a video, fill out a contest entry form,
You pay for prescription drugs, or present you medical care number when visiting the doctor
Collection of personal data may be beneficial for companies and consumers, there is also potential for misuse
Medical Records, Employee Evaluations, etc.
April 10, 2023 Data Mining: Concepts and Techniques
48
Protect Privacy and Data Security
Fair information practices International guidelines for data privacy protection Cover aspects relating to data collection, purpose,
use, quality, openness, individual participation, and accountability
Purpose specification and use limitation Openness: Individuals have the right to know what
information is collected about them, who has access to the data, and how the data are being used
Develop and use data security-enhancing techniques Blind signatures Biometric encryption Anonymous databases
April 10, 2023 Data Mining: Concepts and Techniques
49
Applications and Trends in Data Mining
Data mining applications
Data mining system products and research prototypes
Additional themes on data mining
Social impacts of data mining
Trends in data mining
Summary
April 10, 2023 Data Mining: Concepts and Techniques
50
Trends in Data Mining (1)
Application exploration development of application-specific data mining
system Invisible data mining (mining as built-in function)
Scalable data mining methods Constraint-based mining: use of constraints to
guide data mining systems in their search for interesting patterns
Integration of data mining with database systems, data warehouse systems, and Web database systems
Invisible data mining
April 10, 2023 Data Mining: Concepts and Techniques
51
Trends in Data Mining (2)
Standardization of data mining language A standard will facilitate systematic
development, improve interoperability, and promote the education and use of data mining systems in industry and society
Visual data mining New methods for mining complex types of data
More research is required towards the integration of data mining methods with existing data analysis techniques for the complex types of data
Web mining Privacy protection and information security in data
mining
April 10, 2023 Data Mining: Concepts and Techniques
52
Applications and Trends in Data Mining
Data mining applications
Data mining system products and research prototypes
Additional themes on data mining
Social impacts of data mining
Trends in data mining
Summary
April 10, 2023 Data Mining: Concepts and Techniques
53
Summary
Domain-specific applications include biomedicine (DNA), finance,
retail and telecommunication data mining
There exist some data mining systems and it is important to know
their power and limitations
Visual data mining include data visualization, mining result
visualization, mining process visualization and interactive visual
mining
There are many other scientific and statistical data mining
methods developed but not covered in this book
Also, it is important to study theoretical foundations of data mining
Intelligent query answering can be integrated with mining
It is important to watch privacy and security issues in data mining
April 10, 2023 Data Mining: Concepts and Techniques
54
References (1)
M. Ankerst, C. Elsen, M. Ester, and H.-P. Kriegel. Visual classification: An interactive approach to decision tree construction. KDD'99, San Diego, CA, Aug. 1999.
P. Baldi and S. Brunak. Bioinformatics: The Machine Learning Approach. MIT Press, 1998.
S. Benninga and B. Czaczkes. Financial Modeling. MIT Press, 1997. L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression
Trees. Wadsworth International Group, 1984. M. Berthold and D. J. Hand. Intelligent Data Analysis: An Introduction. Springer-
Verlag, 1999. M. J. A. Berry and G. Linoff. Mastering Data Mining: The Art and Science of
Customer Relationship Management. John Wiley & Sons, 1999. A. Baxevanis and B. F. F. Ouellette. Bioinformatics: A Practical Guide to the
Analysis of Genes and Proteins. John Wiley & Sons, 1998. Q. Chen, M. Hsu, and U. Dayal. A data-warehouse/OLAP framework for scalable
telecommunication tandem traffic analysis. ICDE'00, San Diego, CA, Feb. 2000. W. Cleveland. Visualizing Data. Hobart Press, Summit NJ, 1993. S. Chakrabarti, S. Sarawagi, and B. Dom. Mining surprising patterns using
temporal description length. VLDB'98, New York, NY, Aug. 1998.
April 10, 2023 Data Mining: Concepts and Techniques
55
References (2)
J. L. Devore. Probability and Statistics for Engineering and the Science, 4th ed. Duxbury Press, 1995.
A. J. Dobson. An Introduction to Generalized Linear Models. Chapman and Hall, 1990.
B. Gates. Business @ the Speed of Thought. New York: Warner Books, 1999. M. Goebel and L. Gruenwald. A survey of data mining and knowledge discovery
software tools. SIGKDD Explorations, 1:20-33, 1999. D. Gusfield. Algorithms on Strings, Trees and Sequences, Computer Science and
Computation Biology. Cambridge University Press, New York, 1997. J. Han, Y. Huang, N. Cercone, and Y. Fu. Intelligent query answering by knowledge
discovery techniques. IEEE Trans. Knowledge and Data Engineering, 8:373-390, 1996.
R. C. Higgins. Analysis for Financial Management. Irwin/McGraw-Hill, 1997. C. H. Huberty. Applied Discriminant Analysis. New York: John Wiley & Sons, 1994. T. Imielinski and H. Mannila. A database perspective on knowledge discovery.
Communications of ACM, 39:58-64, 1996. D. A. Keim and H.-P. Kriegel. VisDB: Database exploration using multidimensional
visualization. Computer Graphics and Applications, pages 40-49, Sept. 94.
April 10, 2023 Data Mining: Concepts and Techniques
56
References (3)
J. M. Kleinberg, C. Papadimitriou, and P. Raghavan. A microeconomic view of data mining. Data Mining and Knowledge Discovery, 2:311-324, 1998.
H. Mannila. Methods and problems in data mining. ICDT'99 Delphi, Greece, Jan. 1997.
R. Mattison. Data Warehousing and Data Mining for Telecommunications. Artech House, 1997.
R. G. Miller. Survival Analysis. New York: Wiley, 1981. G. A. Moore. Crossing the Chasm: Marketing and Selling High-Tech Products to
Mainstream Customers. Harperbusiness, 1999. R. H. Shumway. Applied Statistical Time Series Analysis. Prentice Hall, 1988. E. R. Tufte. The Visual Display of Quantitative Information. Graphics Press,
Cheshire, CT, 1983. E. R. Tufte. Envisioning Information. Graphics Press, Cheshire, CT, 1990. E. R. Tufte. Visual Explanations : Images and Quantities, Evidence and
Narrative. Graphics Press, Cheshire, CT, 1997. M. S. Waterman. Introduction to Computational Biology: Maps, Sequences, and
Genomes (Interdisciplinary Statistics). CRC Press, 1995.
April 10, 2023 Data Mining: Concepts and Techniques
57